Maximizing Clearance Rate by Penalizing Redundant Task Assignment in Mobile Crowdsensing Auctions

Gendy, Maggie E.; Al-Kabbany, Ahmad; Badran, Ehab F.

doi:10.1109/wcnc45663.2020.9120614

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…. The proposed comprehensive formulation for data trustworthiness, which counts for both R s i and R h i , lends itself well to be used with other frameworks that aim at maximizing other aspects in MCS, such as the clearance rate (CR) [10]- [12]. In Section VI, we present the results of extensive simulations that compare the performance of various recently proposed CR-maximizing techniques that adopt comprehen-…”

Section: Green Auctioningmentioning

confidence: 99%

“…The area of the simulation was set to m × 1000 m where the tasks and the participants are distributed uniformly. For the auctions, the collective bids of the participants were varied uniformly in the range [1,10], while the values of the tasks were varied in the range [1,5]. The per-task bids were varied uniformly in the range [v j − α, v j + α], and we set α = 2 in our simulations.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…For evaluating the effectiveness of the Green Auctioning algorithm, we developed a green version of three algorithms from the recent literature, namely, TSCM [8], HMCDB [13] and RPB [10], and we compare the performance of the green versions with the original versions. These algorithms are all reputation-aware (RA), but the main difference in the green versions is that they adopt the newly proposed objective With the simulation results shown below, we aim at highlighting the impact of involving the R h as a factor in managing auctions.…”

Section: A Performance Evaluation For Green Auctioningmentioning

confidence: 99%

“…As a performance baseline, we chose the same auctioning techniques that were used to evaluate Green Auctioning, namely, TSCM [8], HMCDB [13], and RPB [10], which are all representatives of reputation-aware (RA) techniques. In the rest of this sub-section, we compare the performance of the original versions of the aforementioned techniques with the performance of their predictive versions.…”

Section: B Performance Evaluation For Predictive Auctioningmentioning

confidence: 99%

“…The research proposed in [9] presented different forms for the reputation factor such as voting (decentralized), statistical (centralized), anchor-assisted decentralized, and collaborative scores, reputation-based auctions. The authors of [10] proposed a redundancy factor to penalize redundant task assignment, where a task is assigned to multiple participants, which can consume the budget unnecessarily and hence decrease the clearance rate (CR) i.e., the percentage of the accomplished sensing tasks in an auction-based campaign [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Green CrowdSensing With Comprehensive Reputation Awareness and Predictive Device-Application Matching Using a New Real-Life Dataset

2020

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Section: Green Auctioningmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: A Performance Evaluation For Green Auctioningmentioning

confidence: 99%

Section: B Performance Evaluation For Predictive Auctioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Green CrowdSensing With Comprehensive Reputation Awareness and Predictive Device-Application Matching Using a New Real-Life Dataset

2020

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Maximizing Clearance Rate of Budget-Constrained Auctions in Participatory Mobile CrowdSensing

2020

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Mobile devices equipped with diverse sensors have emerged as ubiquitous data collection systems within the rising paradigm of Mobile CrowdSensing (MCS). In MCS, auctions are adopted as effective incentive mechanisms in order to secure an acceptable level of contribution from users in participatory MCS. Recent techniques in the literature have addressed several challenges in auctions-based task assignments in centralized MCS. In this research, towards effective task-participant matching, we focus on maximizing the number of completed tasks, the Clearance Rate (CR), which has not been addressed in the literature to date despite the impact it exercises on the satisfaction of service demanders. We propose new bidding procedures for the task allocation strategy. The proposed procedures generalize well to reputation-aware auctioning while handling practical scenarios experienced during campaigns with budget constraints. Particularly, we show that for campaigns that are held consecutively in time, the adoption of an intuitive look-back strategy, for budget transfer from previous campaigns, would remarkably influence the CR. Moreover, observing that tasks with a few bidders should be assigned a higher priority in order to get accomplished, we introduce a new factor for task redundancy. In addition to promoting the accomplishment of unpopular tasks, this factor spares the budget to accomplish more tasks by penalizing redundant task assignment. Extensive performance evaluation of the proposed methods is carried out under various system parameters, namely the number of tasks, auctions, and participants. We demonstrate the effectiveness of the suggested procedures through a significant-and-consistent increase, that ranges from 50% − 500%, in the attained CR compared to the most recent techniques in the literature.

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Multi-armed bandit based device scheduling for crowdsensing in power grids

Zhao

Ni²,

Zhu³

2023

Front. Energy Res.

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With the increase of devices in power grids, a critical challenge emerges on how to collect information from massive devices, as well as how to manage these devices. Mobile crowdsensing is a large-scale sensing paradigm empowered by ubiquitous devices and can achieve more comprehensive observation of the area of interest. However, collecting sensing data from massive devices is not easy due to the scarcity of wireless channel resources and a large amount of sensing data, as well as the different capabilities among devices. To address these challenges, device scheduling is introduced which chooses a part of mobile devices in each time slot, to collect more valuable sensing data. However, the lack of prior knowledge makes the device scheduling task hard, especially when the number of devices is huge. Thus the device scheduling problem is reformulated as a multi-armed bandit (MAB) program, one should guarantee the participation fairness of sensing devices with different coverage regions. To deal with the multi-armed bandit program, a device scheduling algorithm is proposed on the basis of the upper confidence bound policy as well as virtual queue theory. Besides, we conduct the regret analysis and prove the performance regret of the proposed algorithm with a sub-linear growth under certain conditions. Finally, simulation results verify the effectiveness of our proposed algorithm, in terms of performance regret and convergence rate.

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Maximizing Clearance Rate by Penalizing Redundant Task Assignment in Mobile Crowdsensing Auctions

Cited by 4 publications

References 17 publications

Green CrowdSensing With Comprehensive Reputation Awareness and Predictive Device-Application Matching Using a New Real-Life Dataset

Green CrowdSensing With Comprehensive Reputation Awareness and Predictive Device-Application Matching Using a New Real-Life Dataset

Maximizing Clearance Rate of Budget-Constrained Auctions in Participatory Mobile CrowdSensing

Multi-armed bandit based device scheduling for crowdsensing in power grids

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